for envId, env in enumerate(evaluation.envs):
# # Plot histogram for rewards for that env
# if do_plots and interactive:
# env.plotHistogram(evaluation.horizon * evaluation.repetitions)
# (almost) unique hash from the configuration
hashvalue = abs(
hash((tuple(configuration.keys()),
tuple([(len(k) if isinstance(k, (dict, tuple, list)) else k)
for k in configuration.values()]))))
if debug_memory: start_tracemalloc() # DEBUG
# --- Also plotting the history of means
if interactive:
evaluation.plotHistoryOfMeans(envId) # XXX To plot without saving
# Evaluate just that env
evaluation.startOneEnv(envId, env)
# Display the final regrets and rankings for that env
evaluation.printLastRegrets(envId)
evaluation.printFinalRanking(envId)
evaluation.printRunningTimes(envId)
evaluation.printMemoryConsumption(envId)
evaluation.printNumberOfCPDetections(envId)
if debug_memory: display_top_tracemalloc() # DEBUG
# Sub folder with a useful name
subfolder = "SP__K{}_T{}_N{}__{}_algos".format(
env.nbArms, configuration['horizon'], configuration['repetitions'],
from very_simple_configuration import configuration
else:
from example_of_configuration_singleplayer import configuration
configuration['showplot'] = True
evaluation = Evaluator(configuration)
# Start the evaluation and then print final ranking and plot, for each environment
for envId, env in enumerate(evaluation.envs):
# Evaluate just that env
evaluation.startOneEnv(envId, env)
# Compare them
for envId, env in enumerate(evaluation.envs):
evaluation.plotHistoryOfMeans(envId) # XXX To plot without saving
print("\nGiving all the vector of final regrets ...")
evaluation.printLastRegrets(envId)
print("\nGiving the final ranking ...")
evaluation.printFinalRanking(envId)
print("\n\n- Plotting the last regrets...")
evaluation.plotLastRegrets(envId, boxplot=True)
print("\nGiving the mean and std running times ...")
evaluation.printRunningTimes(envId)
evaluation.plotRunningTimes(envId)
print("\nGiving the mean and std running times ...")
evaluation.printMemoryConsumption(envId)